In recent years, the research and development of recommendation technologies for extracting an item which coincides with the user's preference from huge quantities of items and recommending the extracted item to the user are being actively conducted. For instance, digital TVs are equipped with a function of extracting the characteristics of programs viewed or recorded by a user, and recommending similar programs to be broadcast in the future to that user. In the foregoing case, values are given to attributes such as “category, “channel” and “time slot” in program units, values of the respective attributes of the viewed or recorded programs are vectorized as the user's preference information, and future programs to be recommended are also vectorized. Subsequently, the similarity ratio between the vectors of the respective attributes is calculated, and future programs with a high similarity ratio are recommended. When calculating the similarity ratio of the attribute vectors, the method of calculating the Euclid distance or the inner product is used. PTL 1 discloses an invention of analyzing the user's preference based on the user's actions with regard to contents and contents information, and thereby generating preference information.